Difference between revisions of "Team:ETH Zurich/Model/Environment Sensing"

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<h1 class="headline">Environment Sensing: Overview and Goals</h1>
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<h1 class="headline">Tumor Sensor</h1>
 
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<div class="multi-summary">
 
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    <details>
 
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        <summary>GOAL</summary>
 
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        <p>Tune our bacteria so that is only activates the synthesis of Azurin when it is in the right situation. (High d_cell, high lactate)</p>
 
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    </details>
 
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    <p>As discussed in the <a href="https://2017.igem.org/Team:ETH_Zurich/Description">description of our project</a> one main issue of anti-cancer treatments is specificity: deleterious effects induced by these treatments should only target tumor cells, and leave healthy cells unaffected. To avoid such so called “off-target” effects, our synthetic bacterial system comprises two sensing capabilities: sensing of lactate, and of bacterial cell density (via quorum sensing). Because E. coli Nissle should only form colonies in tumors, which are also areas where the ambient lactate concentration is higher than in healthy tissues, we believe that this dual sensing should help to get off-target synthesis of azurin as low as possible.</p>
 
  
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     <p>Our gene design implements the double sensing of cell density and lactate (see FIGURE). We have modeled it to predict its behavior in both in vivo and in vitro situations, depending on relevant genes expression dynamics.</p>
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<h1>GOAL</h1>
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     <p>Tune our bacteria so that it activates the synthesis of Azurin only in the right conditions: High cell density AND High lactate</p>
  
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    <p>Our in vivo model, which integrates parameters estimated from the literature regarding tumor colonization of E. coli Nissle, enabled us to characterize the parameter space where our system would meet both the specificity and Azurin output requirements that we preliminarily set thanks to our Azurin toxicity assays (LINK TO EXPERIMENT)</p>
 
  
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     <p>After we gave preliminary guidelines for the first design of our plasmids based on our parameter space search and previously characterized iGEM BioBrick, we explored which experiments could give us the important parameters of our system. Once determined and conducted in the wet lab, we fitted the obtained data to check where our current system was standing.</p>
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<h1>OVERVIEW</h1>
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    <p>As discussed in the <a href="https://2017.igem.org/Team:ETH_Zurich/Description">description of our project</a> one main issue of anti-cancer treatments is specificity: deleterious effects induced by these treatments should only target tumor cells, and leave healthy cells unaffected. To avoid such so called “off-target” effects, our synthetic bacterial system comprises of two sensing capabilities: sensing of lactate, and of bacterial cell density (via quorum sensing). Because <span class="bacterium">E. coli</span> Nissle should only form colonies in tumors, which are also areas where the ambient lactate concentration is higher than in healthy tissues, we believe that our dual sensing circuit will help minimize off-target synthesis of azurin.</p>
 +
 
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    <p>Our gene design implements the double sensing of cell density and lactate. We have modeled it to predict its behavior in both <i>in vivo</i> and <i>in vitro</i> situations, depending on the relevant gene-expression dynamics.</p>
 +
 
 +
    <p>Our in vivo model, which integrates parameters estimated from the literature regarding tumor colonization of <span class="bacterium">E. coli</span> Nissle, enabled us to <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/system_specifications">set performance criteria</a> and <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space">characterize the parameter space</a> where our system would meet the specificity required to our system</p>
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     <p>After we gave <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space#GuidelineToParts">preliminary guidelines</a> for the first design of our plasmids based on our <a href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space">parameter space search</a> and previously characterized iGEM BioBrick, we used our model to fit the obtained data to characterize the main parameters influencing the behavior of our own system: regarding first <a href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting">the quorum sensing system</a>, and then <a href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Environment_Sensing/AND_gate_fitting">our self-designed hybrid promoters</a>.</p>
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    <p>From a more precise and closer to reality simulation of our system, we implement <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Vivo">a comprehensive <im>in silico</im> model</a> to prove that our system already exhibits <a href="https://2017.igem.org/Team:ETH_Zurich/Model/In_Silico_Final">an very promising performance</a> for the clinical application it has been designed for.</p>
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</section>
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        <div>
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            <figure>
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<img src="https://static.igem.org/mediawiki/2017/f/f4/T--ETH_Zurich--overview_model_sensing.png" alt="" style="width:1000px" usemap="#Map" />
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<map name="Map" id="Map">
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    <area alt="" title="System Specifications" href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/system_specifications" shape="poly" coords="19,363,155,363,159,406,25,407,20,409" />
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    <area alt="" title="Tumor Sensor Model" href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/model" shape="poly" coords="295,182,361,182,365,227,300,227" />
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    <area alt="" title="Functional Parameter Space" href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_space" shape="poly" coords="212,327,353,331,352,411,217,409" />
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    <area alt="" title="Parameter Fitting" href="https://2017.igem.org/wiki/index.php?title=Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting#sec1" shape="poly" coords="656,176,733,176,730,223,659,223" />
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    <area alt="" title="Parameter Fitting" href="https://2017.igem.org/Team:ETH_Zurich/Model/Environment_Sensing/parameter_fitting" shape="poly" coords="642,345,835,342,845,396,637,399" />
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                  <figcaption style="{margin: auto; text-align:center;}">Our modeling process for the <a href="https://2017.igem.org/Team:ETH_Zurich/Experiments/Tumor_Sensor">Tumor Sensor</a></figcation>
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            </figure>
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        </div>
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<section>
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<h1>Going further</h1>
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<p>Now that we have obtained a substantial characterization of our system, and that we now the parameter space in which our system achieve an adequate performance for our application, we can rationally and efficiently fine-tune our system for it to reach an optimal functioning point and accordingly increase its performance.</p>
 
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Latest revision as of 03:40, 2 November 2017

Tumor Sensor

GOAL

Tune our bacteria so that it activates the synthesis of Azurin only in the right conditions: High cell density AND High lactate

OVERVIEW

As discussed in the description of our project one main issue of anti-cancer treatments is specificity: deleterious effects induced by these treatments should only target tumor cells, and leave healthy cells unaffected. To avoid such so called “off-target” effects, our synthetic bacterial system comprises of two sensing capabilities: sensing of lactate, and of bacterial cell density (via quorum sensing). Because E. coli Nissle should only form colonies in tumors, which are also areas where the ambient lactate concentration is higher than in healthy tissues, we believe that our dual sensing circuit will help minimize off-target synthesis of azurin.

Our gene design implements the double sensing of cell density and lactate. We have modeled it to predict its behavior in both in vivo and in vitro situations, depending on the relevant gene-expression dynamics.

Our in vivo model, which integrates parameters estimated from the literature regarding tumor colonization of E. coli Nissle, enabled us to set performance criteria and characterize the parameter space where our system would meet the specificity required to our system

After we gave preliminary guidelines for the first design of our plasmids based on our parameter space search and previously characterized iGEM BioBrick, we used our model to fit the obtained data to characterize the main parameters influencing the behavior of our own system: regarding first the quorum sensing system, and then our self-designed hybrid promoters.

From a more precise and closer to reality simulation of our system, we implement a comprehensive in silico model to prove that our system already exhibits an very promising performance for the clinical application it has been designed for.

Our modeling process for the Tumor Sensor

Going further

Now that we have obtained a substantial characterization of our system, and that we now the parameter space in which our system achieve an adequate performance for our application, we can rationally and efficiently fine-tune our system for it to reach an optimal functioning point and accordingly increase its performance.